Senior Applied Data Scientist

About this role

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Skills you'll use

What you'll do

• End-to-End ML Development: Lead the discovery, development, and deployment of machine learning models (Regression, Classification, Clustering, Time-Series Forecasting) to solve industry-specific challenges. 
• Business Translation: Work closely with stakeholders to identify high-value use cases and translate vague business problems into concrete technical roadmaps. 
• Data Engineering & Wrangling: Design and optimize data pipelines and feature stores, ensuring data quality and integrity across diverse environments (SQL, NoSQL, Data Lakes). 
• Advanced Analytics: Apply statistical rigor to experimental design, including A/B testing and causal inference, to validate the impact of business interventions. 
• Model Productization: Partner with ML Engineers to implement MLOps best practices, including versioning, automated testing, and CI/CD for model deployment. 
• Scalable Architecture: Design robust, reusable analytical frameworks that can be scaled across different client engagements or internal Sia products. 
• Stakeholder Management: Communicate complex technical findings to non-technical executive audiences through compelling storytelling and data visualization. 
• Mentorship: Act as a technical lead, providing code reviews, architectural guidance, and mentorship to junior data scientists within the team. 
• Innovation: Stay at the forefront of the field, evaluating and integrating emerging techniques in Deep Learning, Reinforcement Learning, or Optimization into the Sia toolkits. 
• Education: Master’s or PhD in a quantitative field such as Statistics, Mathematics, Computer Science, Economics, or Physics. 

Who can apply

• Innovation: Stay at the forefront of the field, evaluating and integrating emerging techniques in Deep Learning, Reinforcement Learning, or Optimization into the Sia toolkits. 
• Education: Master’s or PhD in a quantitative field such as Statistics, Mathematics, Computer Science, Economics, or Physics. 
• Experience: 6+ years of professional experience in Data Science, with a proven track record of deploying models into production environments. 
• Core Tech Stack: Expert-level proficiency in Python and its ecosystem (Pandas, NumPy, Scikit-Learn, XGBoost/LightGBM). 
• Deep Learning: Hands-on experience with frameworks like PyTorch or TensorFlow. 
• Cloud Proficiency: Strong experience with cloud-based AI/ML platforms (AWS SageMaker, Azure ML, or Google Vertex AI). 
• AI-Native Engineering Leadership: Experience managing teams that utilize Cursor, GitHub Copilot, or Claude Code as a core part of their daily workflow. 
• Data Manipulation: Advanced SQL skills and experience with big data technologies (Spark, Databricks, or Snowflake). 

Perks & benefits

About Sia

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